Hybrid Precoding Optimization and Private Federated Learning for Future Wireless Systems

This PhD research addresses two challenges in future wireless systems: hybrid precoder design for sub-Terahertz (sub-THz) massive multiple-input multiple-output (MIMO) communications and private federated learning (FL) over wireless channels. The first part of the research introduces a novel hybrid precoding framework that combines true-time delay (TTD) and phase shifters (PS) precoders to counteract the beam squint effect - a significant challenge in sub-THz massive MIMO systems that leads to considerable loss in array gain. Our research presents a novel joint optimization framework for the TTD and PS precoder design, incorporating realistic time delay constraints for each TTD device. We first derive a lower bound on the achievable rate of the system and show that, in the asymptotic regime, the optimal analog precoder that fully compensates for the beam squint is equivalent to the one that maximizes this lower bound. Unlike previous methods, our framework does not rely on the unbounded time delay assumption and optimizes the TTD and PS values jointly to cope with the practical limitations. Furthermore, we determine the minimum number of TTD devices needed to reach a target array gain using our proposed approach. Simulations validate that the proposed approach demonstrates performance enhancement, ensures array gain, and achieves computational efficiency. In the second part, the research devises a differentially private FL algorithm that employs time-varying noise perturbation and optimizes transmit power to counteract privacy risks, particularly those stemming from engineering-inversion attacks. This method harnesses inherent wireless channel noise to strike a balance between privacy protection and learning utility. By strategically designing noise perturbation and power control, our approach not only safeguards user privacy but also upholds the quality of the learned FL model. Additionally, the number of FL iterations is optimized by minimizing the upper bound on the learning error. We conduct simulations to showcase the effectiveness of our approach in terms of DP guarantee and learning utility.